Non-regular electrical stimulation patterns for treating neurological disorders

ABSTRACT

Systems and methods for stimulation of neurological tissue generate stimulation trains with temporal patterns of stimulation, in which the interval between electrical pulses (the inter-pulse intervals) changes or varies over time. Compared to conventional continuous, high rate pulse trains having regular (i.e., constant) inter-pulse intervals, the non-regular (i.e., not constant) pulse patterns or trains that embody features of the invention provide a lower average frequency.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of co-pending U.S. patentapplication Ser. No. 13/897,504, entitled “Non-Regular ElectricalStimulation Patterns for Treating Neurological Disorders,” filed May 20,2013, which is a continuation of co-pending U.S. patent application Ser.No. 12/587,295, filed Oct. 5, 2009, and entitled “Non-Regular ElectricalStimulation Patterns for Treating Neurological Disorders,” which claimedthe benefit of U.S. Provisional Patent Application Ser. No. 61/102,575,filed Oct. 3, 2008, and entitled “Stimulation Patterns For TreatingNeurological Disorders Via Deep Brain Stimulation,” which are bothincorporated herein by reference.

FIELD OF THE INVENTION

This invention relates to systems and methods for stimulating nerves inanimals, including humans.

BACKGROUND OF THE INVENTION

Deep Brain Stimulation (DBS) has been found to be successful in treatinga variety of brain-controlled disorders, including movement disorders.Generally, such treatment involves placement of a DBS type lead into atargeted region of the brain through a burr hole drilled in thepatient's skull, and the application of appropriate stimulation throughthe lead to the targeted region.

Presently, in DBS, beneficial (symptom-relieving) effects are observedprimarily at high stimulation frequencies above 100 Hz that aredelivered in stimulation patterns or trains in which the intervalbetween electrical pulses (the inter-pulse intervals) is constant overtime. The trace of a conventional stimulation train for DBS is shown inFIG. 2. The beneficial effects of DBS on motor symptoms are onlyobserved at high frequencies, while low frequency stimulation mayexacerbate symptoms See Benabid et al. 1991, Limousin et al. 1995.Thalamic DBS at less than or equal to 50 Hz increases tremor in patientswith essential tremor. See Kuncel et al. 2006. Similarly, 50 Hz DBSproduces tremor in pain patients receiving simulation of the ventralposterior medial nucleus of the thalamus (VPM), but the tremordisappears when the frequency is increased. See Constantoyannis 2004.Likewise, DBS of the subthalamic nucleus (STN) at 10 Hz worsens akinesiain patients with Parkinson's disease (PD), while DBS at 130 Hz leads tosignificant improvement in motor function See Timmermann et al. 2004,Fogelson et al. 2005. Similarly, stimulation of the globus pallidus (GP)at or above 130 Hz significantly improves dystonia, whereas stimulationat either 5 or 50 Hz leads to significant worsening. See Kupsch et al.2003.

Model studies also indicate that the masking of pathological burstactivity occurs only with sufficiently high stimulation frequencies. SeeGrill et al. 2004, FIG. 1. Responsiveness of tremor to changes in DBSamplitude and frequency are strongly correlated with the ability ofapplied stimuli to mask neuronal bursting. See Kuncel et al. 2007, FIG.2.

Although effective, conventional high frequency stimulation generatesstronger side-effects than low frequency stimulation, and thetherapeutic window between the voltage that generates the desiredclinical effect(s) and the voltage that generates undesired side effectsdecreases with increasing frequency. Precise lead placement thereforebecomes important. Further, high stimulation frequencies increase powerconsumption. The need for higher frequencies and increased powerconsumption shortens the useful lifetime and/or increases the physicalsize of battery-powered implantable pulse generators. The need forhigher frequencies and increased power consumption requires a largerbattery size, and frequent charging of the battery, if the battery isrechargeable.

SUMMARY OF THE INVENTION

The invention provides stimulation patterns or trains with differenttemporal patterns of stimulation than conventional stimulation trains.The invention also provides methodologies to identify and characterizestimulation patterns or trains that produce desired relief of symptoms,while reducing the average stimulation frequency.

According to one aspect of the invention, the intervals betweenstimulation pulses in a pulse pattern or train (in shorthand called “theinter-pulse intervals”) is not constant over time, but changes or variesover time. These patterns or trains are consequently called in shorthand“non-regular.” According to this aspect of the invention, thenon-regular (i.e., not constant) pulse patterns or trains provide alower average frequency for a given pulse pattern or train, compared toconventional continuous, high rate pulse trains having regular (i.e.,constant) inter-pulse intervals. Having a lower average frequency, thenon-regular stimulus patterns or trains make possible an increase in theefficacy of stimulation by reducing the intensity of side effects; byincreasing the dynamic range between the onset of the desired clinicaleffect(s) and side effects (and thereby reducing sensitivity to theposition of the lead electrode); and by decreasing power consumption,thereby providing a longer useful battery life and/or a smallerimplantable pulse generator, allowing battery size reduction and/or, forrechargeable batteries, longer intervals between recharging.

The non-regular stimulation patterns or trains can be readily applied todeep brain stimulation, to treat a variety of neurological disorders,such as Parkinson's disease, movement disorders, epilepsy, andpsychiatric disorders such as obsessive-compulsion disorder anddepression. The non-regular stimulation patterns or trains can also bereadily applied to other classes electrical stimulation of the nervoussystem including, but not limited to, cortical stimulation, spinal cordstimulation, and peripheral nerve stimulation (including sensory andmotor), to provide the attendant benefits described above and to treatdiseases such as but not limited to Parkinson's Disease, EssentialTremor, Movement Disorders, Dystonia, Epilepsy, Pain, psychiatricdisorders such as Obsessive Compulsive Disorder, Depression, andTourette's Symdrome.

According to another aspect of the invention, systems and methodologiesmake it possible to determine the effects of the temporal pattern of DBSon simulated and measured neuronal activity, as well as motor symptomsin both animals and humans. The methodologies make possible thequalitative determination of the temporal features of stimulationtrains.

The systems and methodologies described herein employ a geneticalgorithm, coupled to a computational model of DBS of the STN, todevelop non-regular patterns of stimulation that produced efficacy (asmeasured by a low error function, E) at lower stimulation frequencies,F. The error function, E, is a quantitative measure from the model whichassesses how faithfully the thalamus transmitted motor commands that aregenerated by inputs from the cortex. A very high correlation existsbetween E and symptoms in persons with PD, and therefore E is a validpredictor for the efficacy of a stimulation train in relieving symptoms(see Dorval et al., 2007).

Previous efforts (see Feng et al. 2007) sought to design stimulationtrains that minimized the total current injection. The systems andmethodologies disclosed herein include an objective function thatmaximizes therapeutic benefit (by minimizing the error function) andimproves stimulation efficiency (by reducing the stimulation frequency),using a model of the STN that reproduces the frequency tuning of symptomreduction that has been documented clinically. In contrast, the Feng etal. model showed, incorrectly, symptom reduction with regular, lowfrequency stimulation. The inventors have identified novel non-regulartemporal patterns of stimulation, while Feng et al. identified regularlow frequency (^(˜)10 Hz) trains that previous clinical work hasdemonstrated to be ineffective.

A pulse generator operatively coupled with at least one electrode, thepulse generator configured to transmit to the electrode an electricalsignal for application to neurological tissue where the electricalsignal may include a waveform shape is shown and described. Theelectrical signal may also include a temporal pattern of stimulationcomprising a repeating succession of non-regular pulse trains, eachpulse train comprising a plurality of single pulses and multiple pulsegroups, with non-regular and non-random inter-pulse intervals betweenthe single pulses and multiple pulse groups, as well as non-regularinter-pulse intervals within the multiple pulse groups themselves, thepulse train repeating in succession to treat a neurological symptom.

A system for neurological tissue stimulation may include an electrodeimplantable in a targeted tissue region and a pulse generator operablycoupled to the electrode, where the pulse generator applies electricalstimulation. The electrical stimulation may include a waveform shape,where the waveform shape is derived based upon a limitation of the pulsegenerator, and a temporal pattern of stimulation comprising a repeatingsuccession of non-regular pulse trains, each pulse train comprising aplurality of single pulses and multiple pulse groups, with non-regularand non-random inter-pulse intervals between the single pulses andmultiple pulse groups, as well as non-regular inter-pulse intervalswithin the multiple pulse groups themselves, the pulse train repeatingin succession to treat a neurological symptom.

A method of treating a neurological condition may include the steps ofselecting a waveform shape based upon a system constraint of a pulsegenerator, and applying a temporal pattern of stimulation to a targetedneurological tissue region using the pulse generator, the temporalpattern of stimulation comprising a plurality of single pulses andmultiple pulse groups, with non-regular and non-random inter-pulseintervals between the single pulses and multiple pulse groups, andnon-regular inter-pulse intervals within the multiple pulse groupsthemselves.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an anatomic view of a system for stimulating tissue of thecentral nervous system that includes an lead implanted in brain tissuecoupled to a pulse generator that is programmed to provide non-regular(i.e., not constant) pulse patterns or trains, in which the intervalbetween electrical pulses (the inter-pulse intervals) changes or variesover time.

FIG. 2 is a diagrammatic trace that shows a conventional regular highfrequency stimulation train, in which the interval between electricalpulses (the inter-pulse intervals) is constant.

FIG. 3 is a diagrammatic trace showing a representative example of arepeating non-regular pulse pattern or train in which the inter-pulseintervals are linearly cyclically ramped over time.

FIGS. 4 and 5 are diagrammatic traces showing other representativeexamples of repeating non-regular pulse patterns or trains comprisingwithin, a single pulse train, a combination of single pulses (singlets)and embedded multiple pulse groups (n-lets), with non-regularinter-pulse intervals between singlets and n-lets as well as non-regularinter-pulse intervals within the multiple pulse n-lets.

FIG. 6 is a graph plotting error fractions (E) for a range ofconstant-frequency deep brain stimulation patterns generated by agenetic algorithm model, used as a baseline for comparison with laternon-constant temporal patterns developed in later batches of the geneticalgorithm.

FIG. 7 is a plot of inter-pulse intervals (IPI's) of a firstgenerational set of Batches 3 to 7 run through the genetic algorithmmodel in which the Cost Function (C) minimized only the error fractions(Cost Function C=E).

FIG. 8 is a graph plotting a new Cost Function C that weighted the errorfraction (E) and average frequency (F) (C=1000*E+F) for the range ofconstant-frequency deep brain stimulation patterns used in FIG. 6, toestablish a new baseline cost for comparison with later non-constanttemporal patterns developed in later batches of the genetic algorithm.

FIG. 9 is a plot of inter-pulse intervals (IPI's) of a next generationalset of Batches 9, 13, 15, 18, and 19 run through the genetic algorithmmodel in which the Cost Function (C) weighted the error fraction (E) andaverage frequency (F) (C=1000*E+F).

FIG. 10 is a plot of inter-pulse intervals (IPI's) of a furthergenerational set of Batches 16, 17, 20, and 21 run through the geneticalgorithm model in which a new Cost Function (C) weighted the averagefrequency more heavily than in FIG. 9 (C=1000*E+2*F).

FIG. 11 is a graphical representation of a generally rectangularwaveform shape.

FIG. 12 is a graphical representation of a generally rising rampwaveform shape.

FIG. 13 is a graphical representation of a generally sinusoidal waveformshape.

FIG. 14 is a graphical representation of a generally decreasingexponential waveform shape.

FIG. 15 is a graphical representation of a generally rising exponentialwaveform shape.

FIG. 16 is a graphical representation of a generally capacitive waveformshape.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 is a system 10 for stimulating tissue of the central nervoussystem. The system includes a lead 12 placed in a desired position incontact with central nervous system tissue. In the illustratedembodiment, the lead 12 is implanted in a region of the brain, such asthe thalamus, subthalamus, or globus pallidus for the purpose of deepbrain stimulation. However, it should be understood, the lead 12 couldbe implanted in, on, or near the spinal cord; or in, on, or near aperipheral nerve (sensory or motor) for the purpose of selectivestimulation to achieve a therapeutic purpose.

The distal end of the lead 12 carries one or more electrodes 14 to applyelectrical pulses to the targeted tissue region. The electrical pulsesare supplied by a pulse generator 16 coupled to the lead 12.

In the illustrated embodiment, the pulse generator 16 is implanted in asuitable location remote from the lead 12, e.g., in the shoulder region.It should be appreciated, however, that the pulse generator 16 could beplaced in other regions of the body or externally.

When implanted, the case of the pulse generator can serve as a referenceor return electrode. Alternatively, the lead 12 can include a referenceor return electrode (comprising a bi-polar arrangement), or a separatereference or return electrode can be implanted or attached elsewhere onthe body (comprising a mono-polar arrangement).

The pulse generator 16 includes an on-board, programmable microprocessor18, which carries embedded code. The code expresses pre-programmed rulesor algorithms under which a desired electrical stimulation waveformpattern or train is generated and distributed to the electrode(s) 14 onthe lead 12. According to these programmed rules, the pulse generator 16directs the prescribed stimulation waveform patterns or trains throughthe lead 12 to the electrode(s) 14, which serve to selectively stimulatethe targeted tissue region. The code is preprogrammed by a clinician toachieve the particular physiologic response desired.

In the illustrated embodiment, an on-board battery 20 supplies power tothe microprocessor 18. Currently, batteries 20 must be replaced every 1to 9 years, depending on the stimulation parameters needed to treat adisorder. When the battery life ends, the replacement of batteriesrequires another invasive surgical procedure to gain access to theimplanted pulse generator. As will be described, the system 10 makespossible, among its several benefits, an increase in battery life.

The stimulation waveform pattern or train generated by the pulsegenerator differs from convention pulse patterns or trains in that thewaveform comprises repeating non-regular (i.e., not constant) pulsepatterns or trains, in which the interval between electrical pulses (theinter-pulse intervals or IPI) changes or varies over time. Examples ofthese repeating non-regular pulse patterns or trains are shown in FIGS.3 to 5. Compared to conventional pulse trains having regular (i.e.,constant) inter-pulse intervals (as shown in FIG. 2), the non-regular(i.e., not constant) pulse patterns or trains provide a lower averagefrequency for a given pulse pattern or train, where the averagefrequency for a given pulse train (expressed in hertz or Hz) is definedas the sum of the inter-pulse intervals for the pulse train in seconds(Σ_(IPI)) divided by the number of pulses (n) in the given pulse train,or (Σ_(IPI))/n. A lower average frequency makes possible a reduction inthe intensity of side effects, as well as an increase in the dynamicrange between the onset of the desired clinical effect(s) and sideeffects, thereby increasing the clinical efficacy and reducingsensitivity to the position of the electrode(s). A lower averagefrequency brought about by a non-regular pulse pattern or train alsoleads to a decrease in power consumption, thereby prolonging batterylife and reducing battery size.

The repeating non-regular (i.e., not constant) pulse patterns or trainscan take a variety of different forms. For example, as will be describedin greater detail later, the inter-pulse intervals can be linearlycyclically ramped over time in non-regular temporal patterns (growinglarger and/or smaller or a combination of each over time); or beperiodically embedded in non-regular temporal patterns comprisingclusters or groups of multiple pulses (called n-lets), wherein n is twoor more. For example, when n=2, the n-let can be called a doublet; whenn=3, the n-let can be called a triplet; when n=4, the n-let can becalled a quadlet; and so on. The repeating non-regular pulse patterns ortrains can comprise combinations of single pulses (called singlets)spaced apart by varying non-regular inter-pulse intervals and n-letsinterspersed among the singlets, the n-lets themselves being spacedapart by varying non-regular inter-pulse intervals both between adjacentn-lets and between the n pulses embedded in the n-let. If desired, thenon-regularity of the pulse pattern or train can be accompanied byconcomitant changes in waveform and/or amplitude, and/or duration ineach pulse pattern or train or in successive pulse patterns or trains.

Each pulse comprising a singlet or imbedded in an n-let in a given traincomprises a waveform that can be monophasic, biphasic, or multiphasic.Each waveform possesses a given amplitude (expressed, e.g., in amperes)that can, by way of example, range from 10 μa (E⁻⁶) to 10 ma (E⁻³). Theamplitude of a given phase in a waveform can be the same or differ amongthe phases. Each waveform also possesses a duration (expressed, e.g., inseconds) that can, by way of example, range from 10 μs (E⁻⁶) to 2 ms(E⁻³). The duration of the phases in a given waveform can likewise bethe same or different. It is emphasized that all numerical valuesexpressed herein are given by way of example only. They can be varied,increased or decreased, according to the clinical objectives.

When applied in deep brain stimulation, it is believed that repeatingstimulation patterns or trains applied with non-regular inter-pulseintervals can regularize the output of disordered neuronal firing, tothereby prevent the generation and propagation of bursting activity witha lower average stimulation frequency than required with conventionalconstant frequency trains, i.e., with a lower average frequency thanabout 100 Hz.

FIG. 3 shows a representative example of a repeating non-regular pulsepattern or train in which the inter-pulse intervals are linearlycyclically ramped over time. As shown in FIG. 3, the pulse pattern ortrain includes singlet pulses (singlets) spaced apart by progressivelyincreasing inter-pulse intervals providing a decrease in frequency overtime, e.g., having an initial instantaneous frequency of 140 Hz,decreasing with doubling inter-pulse intervals, to a final instantaneousfrequency of 40 Hz. The inter-pulse intervals can vary within aspecified range selected based upon clinical objections, e.g., not toexceed 25 ms, or not to exceed 100 ms, or not to exceed 200 ms, to takeinto account burst responses and subsequent disruption of thalamicfidelity.). The non-regular pulse trains repeat themselves for aclinically appropriate period of time. As shown in FIG. 3, the firstpulse train comprises progressively increasing inter-pulse intervalsfrom smallest to largest, followed immediately by another essentiallyidentical second pulse train comprising progressively increasinginter-pulse intervals from smallest to largest, followed immediately byan essentially identical third pulse train, and so on. Therefore,between successive pulse trains, there is an instantaneous change fromthe largest inter-pulse interval (at the end of one train) to thesmallest inter-pulse interval (at the beginning of the next successivetrain). The train shown in FIG. 3 has an average frequency of 85 Hz andis highly non-regular, with a coefficient of variation (CV) of about0.5. As is demonstrated in the following Example (Batch 3), theincreased efficiency of the pulse train shown in FIG. 3 (due to thelower average frequency) also can provide greater efficacy, as comparedto a constant 100 Hz pulse pattern.

The train shown in FIG. 3 exploits the dynamics of burst generation inthalamic neurons. The early high frequency phase of the train masksintrinsic activity in subthalamic nucleus (STN) neurons, and theinter-pulse interval increases reduce the average frequency. A family oftrains can be provided by varying the initial frequency, finalfrequency, and rate of change within the train, with the objective toprevent thalamic bursting with a lower average stimulation frequencythan required with constant frequency trains.

FIGS. 4 and 5 show other representative examples of repeatingnon-regular pulse patterns or trains. The pulse trains in FIGS. 4 and 5comprise within, a single pulse train, a combination of single pulses(singlets) and embedded multiple pulse groups (n-lets), with non-regularinter-pulse intervals between singlets and n-lets, as well asnon-regular inter-pulse intervals within the n-lets themselves. Thenon-regular pulse trains repeat themselves for a clinically appropriateperiod of time.

The non-regular pulse train can be characterized as comprising one ormore singlets spaced apart by a minimum inter-pulse singlet interval andone or more n-lets comprising, for each n-let, two or more pulses spacedapart by an inter-pulse interval (called the “n-let inter-pulseinterval”) that is less than the minimum singlet inter-pulse interval.The n-let inter-pulse interval can itself vary within the train, as canthe interval between successive n-lets or a successive n-lets andsinglets. The non-regular pulse trains comprising singlets and n-letsrepeat themselves for a clinically appropriate period of time.

In FIG. 4, each pulse train comprises four singlets in succession (withnon-regular inter-pulse intervals there between); followed by fourdoublets in succession (with non-regular inter-doublet pulse intervalsthere between and non-regular inter-pulse intervals within each n-let);followed by a singlet, three doublets, and a singlet (with non-regularinter-pulse intervals there between and non-regular inter-pulseintervals within each n-let). The temporal pattern of this pulse trainrepeats itself in succession for a clinically appropriate period oftime. The non-regular temporal pulse pattern shown in FIG. 4 has anaverage frequency of 67.82 Hz without loss of efficacy, as isdemonstrated in the following Example, Batch 17.

In FIG. 5, each pulse train comprises four singlets in succession (withnon-regular inter-pulse intervals there between); followed by threedoublets in succession (with non-regular inter-doublet pulse intervalsthere between and non-regular inter-pulse intervals within each n-let).The temporal pattern of this pulse train repeats itself in successionfor a clinically appropriate period of time. The non-regular temporalpulse pattern shown in FIG. 5 has an average frequency of 87.62 Hzwithout loss of efficacy, as is demonstrated in the following Example,Batch 18.

The following Example illustrates a representative methodology fordeveloping and identifying candidate non-regular stimulation trains asshown in FIGS. 3 to 5 that achieve comparable or better efficacy at alower average frequency (i.e., more efficiency) than constantinter-pulse interval trains.

EXAMPLE

Computational models of thalamic DBS (McIntyre et al. 2004, Birdno,2009) and subthalamic DBS (Rubin and Terman, 2004) can be used withgenetic-algorithm-based optimization (Davis, 1991) (GA) to designnon-regular stimulation patterns or trains that produce desired reliefof symptoms with a lower average stimulation frequency than regular,high-rate stimulation. McIntyre et al. 2004, Birdno, 2009; Rubin andTerman, 2004; and Davis, 1991 are incorporated herein by reference.

In the GA implementation, the stimulus train (pattern) is the chromosomeof the organism, and each gene in the chromosome is the IPI between twosuccessive pulses in the train. The implementation can start, e.g., withtrains of 21 pulses (20 genes) yielding a train length of ^(˜)400 ms (ataverage frequency of 50 Hz), and the 6 s trains required for stimulationare built by serial concatenation of 15 identical pulse trains. Theprocess can start with an initial population of, e.g., 50 organisms,constituted of random IPI's drawn from a uniform distribution. At eachstep (generation) of the GA, the fitness of each pulse train isevaluated using either the TC or basal ganglia network model (identifiedabove) and calculating a cost function, C. From each generation, the 10best stimulus trains (lowest C) are selected, to be carried forward tothe next generation. They will also be combined (mated) and randomvariations (mutations) introduced into the 40 offspring, yielding 50trains in each generation. This process assures that the beststimulation trains (traits) are carried through to the next generation,while avoiding local minima (i.e., mating and mutations preserve geneticdiversity). See Grefenstette 1986. The GA continues through successivegenerations until the median and minimum values of the cost functionreach a plateau, and this will yield candidate trains.

The objective is to find patterns of non-constant inter-pulse intervaldeep brain stimulation trains that provide advantageous results, asdefined by low frequency and low error rate. An error function isdesirably created that assigns the output of each temporal pattern ofstimulation a specific error fraction (E) based on how the voltageoutput of the thalamic cells correspond to the timing of the inputstimulus. Using this error fraction, a cost function (C) is desirablycreated to minimize both frequency and error fraction, according torepresentative equation C=W*E+K*f, where C is the cost, E is the errorfraction, f is the average frequency of the temporal pattern ofstimulation, W is an appropriate weighting factor for the errorfunction, and K is an appropriate weighting factor for the frequency.The weighting factors W and K allow quantitative differentiation betweenefficacy (E) and efficiency (f) to generate patterns of non-constantinter-pulse interval deep brain stimulation trains that provideadvantageous results with lower average frequencies, compared toconventional constant frequency pulse trains.

With this cost function, the voltage output of several candidatetemporal patterns of stimulation can be evaluated and the costcalculated. Temporal patterns of stimulation with a low cost can then beused to create new temporal patterns of similar features in an attemptto achieve even lower costs. In this way, new temporal patterns ofstimulation can be “bred” for a set number of generations and the besttemporal patterns of stimulation of each batch recorded.

Several batches of the genetic algorithm yields useful results in thatthey achieve lower costs than the corresponding constant frequency DBSwaveforms. Some batches can be run in an attempt to find especially lowfrequency temporal patterns of stimulation, by changing the costfunction to weight frequency more heavily, or vice versa (i.e., bychanging W and/or K). These batches can also yield lower cost resultsthan the constant-frequency waveforms.

By way of example, a total of 14 batches of the genetic algorithm wererun and evaluated with various cost functions and modified initialparameters.

Before the trials were run, a baseline was established by runningconstant-frequency patterns of stimulation through the model andanalyzing the associated error fractions (FIG. 6). As can be seen fromFIG. 6, the healthy condition produced a low error fraction of 0.1 whilethe Parkinsonian condition without DBS yielded a higher error fractionof 0.5. From these results, constant high-frequency patterns ofstimulation ranging from 100-200 Hz gave near perfect results. Novelnon-constant temporal patterns of stimulation would then be consideredadvantageous if they showed error fractions very close to 0.1 withaverage frequencies less than 100-200 Hz.

The first set of batches was run by minimizing only the error fraction(E). Thus, the associated cost function was simply C=E. The results aresummarized according to average frequency and error fraction (ExampleTable 1). The associated inter-pulse intervals (IPI's) can be seen inFIG. 7. Batch 3 outputted an error fraction 0.054. Another interestingfeature is that the IPI's in Batch 3 gradually increased until about 40msec, and then repeated itself. This provides support that ramp trainsare advantageous. The trace shown in FIG. 3 generally incorporates thetemporal features of Batch 3.

The remaining batches yielded error fractions higher than 0.1 and wereno better than the 150 Hz constant-frequency case.

EXAMPLE TABLE 1 Error Fraction Only, C = E # Average Frequency ErrorFraction IPI Length 3 127.5 0.054 5 4 95.62 0.162 39 5 113.6 0.139 13 694.64 0.132 26 7 101.6 0.142 31

Because many batches were yielding error fractions above 0.1 (healthycondition), and only a small window of error fraction less than the 150Hz DBS case would be useful, a new cost function was constructed tominimize an alternate feature of the temporal patterns of stimulation;namely, frequency. This new cost function weighted the error fractionand frequency, yielding the equation C=1000*E+F, where C is cost, E iserror fraction, and F is the average frequency of the waveform in Hz,W=1000, and K=1.

In order to establish a new baseline cost, the constant-frequencypatterns of stimulation were evaluated again according to the new costfunction (FIG. 8). As can be seen from the graph, the healthy conditionreported a cost of 90.65 and the Parkinson case with no DBS yielded505.50. The best constant-frequency pattern of stimulation with the newcost function was the 100 Hz case with a cost of 231.11. This new cost′function allowed for a wider range of solutions, because a temporalpattern of stimulation would be considered useful if it had a cost lessthan 231.11 but presumably higher than 90.65.

The results of the new cost function can be seen in Example Table 2 andthe IPI's visualized in FIG. 9. The best results were seen in batches 15and 18, which had the lowest costs. Batch 18 is interesting in that italso exhibits a ramp-like pattern of increasing interpulse intervals. Itshows a steadily falling IPI, followed by a sudden rise, and then aquick fall, rise, and fall-almost as if it consists of 3 smaller ramps.The trace shown in FIG. 5 generally incorporates the temporal featuresof Batch 18. Batch 15 also performed very well, but its qualitativefeatures are more difficult to discern.

EXAMPLE TABLE 2 Cost Function, C = 1000*E + F Average # Frequency IPILength Error Fraction Cost 9 94.74 34 0.124 218.8 13 132.9 12 0.087219.4 15 98.00 17 0.098 196.0 18 81.28 10 0.116 197.3 19 84.70 20 0.116201.2

The advantage of low frequency was emphasized with a new cost function,which weighted frequency more heavily, C=1000*E+2*F. Because thefrequency of DBS does not affect the healthy condition or the PD with noDBS, these baseline costs stayed the same at 90.65 and 505.50,respectively. The 100 Hz was again the best constant-frequency temporalpattern of stimulation, with a cost of 331.11. The following temporalpatterns of stimulation, then, were considered useful if they had lowfrequencies and costs less than 331.11 and greater than 90.65.

The results of the revised cost function can be seen in Example Table 3and the IPI's visualized in FIG. 10. Of the resulting batches, batch 17proved most interesting because of its very low average frequency of67.82 Hz. Even with such a low frequency, it managed to prove betterthan the 100 Hz condition with a reduction in cost of about 10. Thewaveform of batch 17 is interesting in that it consists of a ramppattern of decreasing IPI in the first 100 msec, followed by continualshift between large IPI and small IPI. The qualitative feature ofquickly changing between large and small IPI's may prove advantageous.The trace shown in FIG. 4 generally incorporates the temporal featuresof Batch 17.

EXAMPLE TABLE 3 Revised Cost Function, Cost = 1000*E + 2*F Average #Frequency IPI Length Error Fraction Cost 16 84.92 47 0.239 323.8 1767.82 20 0.253 321.1 20 79.25 10 0.236 315.4 21 77.15 20 0.269 346.6

The most interesting temporal patterns of stimulation in this Exampleare from batches 15, 17, and 18. Batch 15 produced a temporal pattern ofstimulation with an average frequency of 98 Hz with an error fraction aslow as 0.098. Thus, it outperformed the 100 Hz constant-frequency caseby managing to lower the error even further at roughly the samefrequency. Still, the qualitatively useful features of batch 15 aredifficult to discern. Batch 17 was also appealing because of its verylow frequency of 67.82. This low frequency was gained at the cost ofincreased error at 0.253, but it may nonetheless be useful if emphasisis placed on maintaining low frequency DBS. The qualitative features ofbatch 17 indicated at first a ramp followed by a continual switchingbetween low and high IPI's. Lastly, batch 18 stood somewhere in themiddle with a fairly low frequency of 87.62 and low error fraction of0.116, only marginally higher than the healthy condition of 0.1. Thedominant qualitative feature of batch 18's waveform is that it too showsa ramp nature in that the IPI initially steadily falls, then quicklyrises, falls, and then rises. The rapid changing between high and lowIPI of batch 17 can be envisioned as a set of steep ramps.

A comparison of Batch 17 (FIG. 4) and Batch 18 (FIG. 5) demonstrates howthe balance between efficacy (E) and efficiency (f) in non-regulartemporal patterns of stimulation can be purposefully tailored to meetclinical objectives. The systems and methodologies discussed allowchanging the cost function by weighting efficacy (E) or frequency (f)more heavily (i.e., by changing W and/or K), while still yieldingtemporal patterns of stimulation with lower cost results than theconstant-frequency waveforms. Comparing Batch 17 with Batch 18, one seesthat the error fraction (E) (i.e., the efficacy of the temporal pattern)of Batch 17 (0.253) is greater than the error fraction (E) (i.e., theefficacy of the temporal pattern) of Batch 18 (0.116). However, one canalso see that the efficiency (i.e., the average frequency) of Batch 17(67.82 Hz) is lower than the efficiency (i.e., the average frequency) ofBatch 18 (81.28 Hz). Through different in terms of efficacy andefficiency, both Batch 17 and Batch 18 have costs better thanconstant-frequency temporal patterns.

The non-regular temporal patterns of stimulation generated and disclosedabove therefore make possible achieving at least the same or equivalent(and expectedly better) clinical efficacy at a lower average frequencycompared to conventional constant-frequency temporal patterns. The loweraverage frequencies of the non-regular temporal stimulation patternsmake possible increases in efficiency and expand the therapeutic windowof amplitudes that can be applied to achieve the desired result beforeside effects are encountered.

DBS is a well-established therapy for treatment of movement disorders,but the lack of understanding of mechanisms of action has limited fulldevelopment and optimization of this treatment. Previous studies havefocused on DBS-induced increases or decreases in neuronal firing ratesin the basal ganglia and thalamus. However, recent data suggest thatchanges in neuronal firing patterns may be at least as important aschanges in firing rates.

The above described systems and methodologies make it possible todetermine the effects of the temporal pattern of DBS on simulated andmeasured neuronal activity, as well as motor symptoms in both animalsand humans. The methodologies make possible the qualitative andquantitative determination of the temporal features of low frequencystimulation trains that preserve efficacy.

The systems and methodologies described herein provide robust insightinto the effects of the temporal patterns of DBS, and thereby illuminatethe mechanisms of action. Exploiting this understanding, new temporalpatterns of stimulation can be developed, using model-basedoptimization, and tested, with the objective and expectation to increaseDBS' efficacy and increase DBS efficiency by reducing DBS side effects.

The present teachings provide non-regular stimulation patterns or trainsthat may create a range of motor effects from exacerbation of symptomsto relief of symptoms. The non-regular stimulation patterns or trainsdescribed herein and their testing according to the methodologydescribed herein will facilitate the selection of optimal surgicaltargets as well as treatments for new disorders. The non-regularstimulation patterns or trains described herein make possible improvedoutcomes of DBS by reducing side effects and prolonging battery life.

Another important consideration to improve efficiency and/or efficacy ofthe stimulation applied is to effectively utilize the waveform generatedby the applicable pulse generator. Waveform shapes may be modified toprovide different elements of control to the stimulation. Exemplaryembodiments of a method of electing an applicable waveform shape tostimulation is described in U.S. patent application Ser. No. 13/118,081,entitled “Waveform Shapes for Treating Neurological Disorders Optimizedfor Energy Efficiency,” which is hereby incorporated by reference. Byway of a non-limiting example, a global optimization algorithm, such asa genetic algorithm, may be utilized to determine a waveform shape to beapplied by the pulse generator 16. Such waveform shape or shapes may beselected to improve the efficiency, efficacy or both of the pulsegenerator 16.

The waveform shape applied, however, may often be limited by system andhardware constraints of the pulse generator 16 applying the stimulation,e.g., the constraints or limitations of the memory, power source and/ormicroprocessor of the pulse generator. There may be occasion that apre-defined pulse generator 16 may be desired to be used in the methoddescribed above for finding patterns of non-constant inter-pulseinterval deep brain stimulation trains that are either incapable ofapplying the desired waveform shape or the application of such waveformshape may not meet the specified goals of efficiency and/or efficacy.

In such situations, the waveform shape may be limited by the pulsegenerator 16 applying such. For example, the microprocessor positionedwithin the pulse generator 16 may have limitations as to the waveformshape of the electrical stimulation it is able to apply, such as themicroprocessor having limited memory, limited functionality and thelike. Similarly, the power source of the pulse generator 16 may limitthe waveform shapes that may be applied by the specific pulse generator16 efficiently and/or effectively. Also, the memory of the pulsegenerator 16 may limit the waveform shapes that may be applied by thespecific pulse generator 16 efficiently and/or effectively.

It may be desired, therefore, that the waveform shape may be chosen tobe the most beneficial to the pulse generator 16 being utilized. Abeneficial waveform shape may be one that is easy for the pulsegenerator 16 to generate and/or one that is efficient to generate by theapplicable pulse generator 16. The actual waveform shape may changebased upon the type, specifications, parameters, or functionality of thepulse generator 16 utilized. Therefore, it may be desired for differentpulse generators 16 to apply different waveform shapes to the electricalstimulation. Further, it may be desirable that the pulse generator 16apply different waveform shapes to improve efficacy, efficiency or bothof the stimulation. This is of particular importance to extend batterylife of the applicable pulse generator 16, i.e., the life of the powersource. As noted above, extending the life of the pulse generator 16 mayallow a patient to undergo fewer pulse generator replacement surgeries,which saves money, avoids complications from surgery and reducesdiscomfort to the patient.

In some embodiments, the waveform shape applied by the pulse generator16 may be generally rectangular see FIG. 11. This rectangular waveformshape may be optimal for some pulse generators 16, but may not beoptimal for other pulse generators 16, i.e., it may take more energythan desired to apply such waveform shape. For those pulse generators 16that such rectangular waveform shape is not optimal a different shapedwaveform shape may be chosen and applied by the pulse generator 16. Byway of a non-limiting example, an optimization method—such as throughuse of an algorithm, including, without limitation a genetic algorithmas described in U.S. patent application Ser. No. 13/118,081—may beutilized to determine the optimal waveform shape based upon the pulsegenerator 16 applying the stimulation. In addition or alternatively, atrial and error approach may be utilized. Further still, the waveformshape may be specific to the pulse generator 16 utilized, i.e., whatwaveform shape can the pulse generator generate efficiently. Suchconsiderations may include the limitations of the memory, power sourceor microprocessor of the pulse generator 16. Regardless of the approach,once the applicable waveform shape is identified, the applicable pulsegenerator 16 may be modified—such as through programming such—to applythe predetermined waveform shape.

By way of a non-limiting example, a clinician may elect to utilize arising ramp waveform shape such as shown in FIG. 12. The clinician maymodify the pulse generator 16 to apply such waveform shape or utilize apulse generator 16 that efficiently utilizes such waveform shape—theclinician or a technician may program the pulse generator 16 to outputthe predefined waveform shape. Such programming may be accomplisheddirectly on the pulse generator 16 or by operatively coupling the pulsegenerator 16 to a computer source (e.g., a desktop computer, laptop ortablet). The clinician may evaluate the applied waveform shape and mayeither elect to utilize such in applying the therapy or chose anotherwaveform shape. Examples of potential waveform shapes are shown in FIGS.11-16. It should be understood that the waveform shapes shown anddescribed herein are merely exemplary and the present teachings are notlimited to those waveform shapes shown and described herein. Anyappropriate waveform shape may be utilized without departing from thepresent teachings.

As identified above, the waveform shape may be limited by the pulsegenerator 16 being utilized. In such embodiments, an easier to generatewaveform shape—such as a capacitive shaped waveform shown in FIG. 16—maybe the most efficient waveform shape for the particular pulse generator16 utilized. By way of a non-limiting example, such capacitive shapedwaveform may have an efficiency of approximately 99.5%. In theseembodiments, the pulse generator 16 utilized drives the election of thewaveform shape. The applicable waveform shape may be chosen so that itis easy for the pulse generator 16 to produce, efficiently created bythe pulse generator 16, or a combination of such. Further still, aparticular waveform shape may be chosen so that a smaller or moresimplified pulse generator may be utilized to apply the stimulation.

A smaller pulse generator 16, especially one that is implanted into apatient is beneficial for the patient—it may take up less space withinthe patient, be tolerated better by the patient, require less cutting toinsert or any combination of these factors. Further, a more simplifiedpulse generator may be beneficial to increase the life span that suchpulse generator is able to operatively function. This may benefitpatients, especially those undergoing long term therapies by reducingthe number of times that the pulse generator may need to be replaced,which reduces the number of surgeries required over the life of thepatient.

Regardless of the method of determining the applicable waveform shape,once selected by the clinician and programmed into the pulse generator16, the method described above to elect the appropriate temporal patternof stimulation, i.e., the appropriate non-regular stimulation patternsor trains may be utilized. The appropriate non-regular stimulationpatterns or trains may depend or otherwise relate in some manner to thewaveform shape selected. Specifically, the clinician may elect aparticular waveform shape for the pulse generator 16 to apply. As notedabove, the shape of such may be selected based upon the limitations ofthe pulse generator being utilized.

The method described above may be utilized to find patterns ofnon-constant inter-pulse interval deep brain stimulation trains thatprovide advantageous results, as defined by low frequency and low errorrate. By way of a non-limiting example, genetic algorithms may beutilized to take the elected waveform shape and determine an improvedpattern of non-constant inter-pulse intervals of deep brain stimulationtrains. These non-constant inter-pulse intervals of deep brainstimulation trains may improve the efficiency and/or efficacy of theelected waveform shape, which may improve the efficiency and/or efficacyof the stimulation. This may allow a smaller or less robust pulsegenerator 16 to be utilized, provide a more efficacious result, providea more efficient stimulation, or any combination of such. These factorsmay reduce the overall cost in implementing such electrical stimulation,may reduce the overall number of surgeries required over the life of thepatient, and may provide a more beneficial result to the patient.

LITERATURE CITATIONS

-   Benabid A L, Pollak P, Gervason C, Hoffmann D, Gao D M, Hommel M,    Perret J E, de Rougemont J (1991) Long-term suppression of tremor by    chronic stimulation of the ventral intermediate thalamic nucleus.    Lancet. 337:403-6.-   Birdno M J “Analyzing the mechanisms of thalamic deep brain    stimulation: computational and clinical studies”. Ph.D.    Dissertation. Department of Biomedical Engineering, Duke University,    Durham, N.C., USA, August 2009.-   Constantoyannis C, Kumar A, Stoessl A J, Honey C R (2004) Tremor    induced by thalamic deep brain stimulation in patients with complex    regional facial pain. Mov Disord. 19:933-936.-   Davis L (1991) Handbook of genetic algorithms. Van Nostrand    Reinhold, N.Y.-   Dorval A D, Kuncel A M, Birdno M J, Turner D A, Grill W M (2007)    Deep brain stimulation alleviates Parkinsonian bradykinesia by    regularizing thalamic throughput in human subjects. Society for    Neuroscience Abstracts 32.-   Feng X J, Shea-Brown E, Greenwald B, Kosut R, Rabitz H (2007)    Optimal deep brain stimulation of the subthalamic nucleus-a    computational study. J Comput Neurosci. 23(3):265-282.-   Fogelson N, Kuhn A A, Silberstein P, Limousin P D, Hariz M,    Trottenberg T, Kupsch A, Brown P (2005) Frequency dependent effects    of subthalamic nucleus stimulation in Parkinson's disease.    Neuroscience Letters 382:5-9.-   Grefenstette J J (1986) Optimization of Control Parameters for    Genetic Algorithms. IEEE Transactions on Systems, Man and    Cybernetics 16:122-128.-   Grill W M, Cooper S E, Montgomery E B (2003) Effect of stimulus    waveform on tremor suppression and paresthesias evoked by thalamic    deep brain stimulation. Society for Neuroscience Abstracts 29.-   Kuncel A M, Cooper S E, Montgomery E B, Baker K B, Rezai A R, Grill    W M (2006) Clinical response to varying the stimulus parameters in    deep brain stimulation for essential tremor. Movement Disorders    21(11):1920-1928.-   Kupsch A, Klaffke S, Kuhn A A, Meissner W, Arnold G, Schneider G H,    Maier-Hauff K, Trottenberg T (2003) The effects of frequency in    pallidal deep brain stimulation for primary dystonia. J Neurol    250:1201-1204.-   Limousin P, Pollack P, Benazzouz A (1995) Effect on Parkinsonian    signs and symptoms of bilateral stimulation. The Lancet 345:91-95.-   McIntyre C C, Grill W M, Sherman D L, Thakor N V (2004) Cellular    effects of deep brain stimulation: model-based analysis of    activation and inhibition. J Neurophysiol 91:1457-1469.-   Rubin J E, Terman D (2004) High frequency stimulation of the    subthalamic nucleus eliminates pathological thalamic rhythmicity in    a computational model. J Comput Neurosci 16:211-235.-   Timmermann L, Gross J, Dirks M, Volkmann J, Freund H J, Schnitzler    A (2003) The cerebral oscillatory network of parkinsonian resting    tremor. Brain, 126:199-212.

Having thus described the invention, the following is claimed:
 1. Apulse generator operatively coupled with at least one electrode, thepulse generator configured to transmit to the electrode an electricalsignal for application to neurological tissue, the electrical signalcomprising: a waveform shape related to the pulse generator; and atemporal pattern of stimulation comprising a repeating succession ofnon-regular pulse trains, each pulse train comprising a plurality ofpulses having non-regular, non-random, differing inter-pulse intervalstherebetween, the pulse train repeating in succession to treat aneurological symptom.
 2. The pulse generator of claim 2, wherein thewaveform shape comprises at least one of rectangular, rising ramp,sinusoid, decreasing exponential, rising exponential, and capacitive. 3.The pulse generator of claim 1, further comprising a microprocessoroperatively coupled with the electrode, wherein the waveform shaperelates to a capacity of the microprocessor.
 4. The pulse generator ofclaim 1, further comprising a power source, wherein the waveform shapeis limited by capacity of the power source.
 5. The pulse generator ofclaim 1, wherein the waveform shape is optimized by an optimizationmodel to achieve a first cost function.
 6. The pulse generator of claim5, wherein the optimization model includes a genetic algorithm.
 7. Thepulse generator of claim 5, wherein the waveform shape is optimized bythe optimization model to achieve a second cost function, the first costfunction different from the second cost function.
 8. A system forneurological tissue stimulation comprising: an electrode implantable ina targeted tissue; a pulse generator operably coupled to the electrode,wherein the pulse generator applies electrical stimulation, theelectrical stimulation comprising: a waveform shape, wherein thewaveform shape is derived based upon a limitation of the pulsegenerator; and a temporal pattern of stimulation comprising a repeatingsuccession of non-regular pulse trains, each pulse train comprising aplurality of pulses having non-regular, non-random, differinginter-pulse intervals therebetween, the pulse train repeating insuccession to treat a neurological symptom.
 9. The system of claim 8,wherein the waveform shape is derived through use of a globaloptimization algorithm to meet a predetermined treatment thresholdwhereby the stimulation waveform is deep brain stimulation applied toprovide relief for a neurological symptom.
 10. The system of claim 8,wherein the limitation of the pulse generator is a capacity of the pulsegenerator.
 11. The system of claim 8, wherein the waveform shape resultsin an efficiency of about 99.5% for the pulse generator.
 12. The systemof claim 8, wherein the waveform shape results in an efficiency of aboutbetween 95% and 99.5% for the pulse generator.
 13. The system of claim8, wherein the waveform shape is derived through use of a computationalmodel to meet a first predetermined cost function.
 14. The system ofclaim 13, further comprising a second wave form shape derived throughuse of the computational model to meet a second predetermined costfunction.
 15. The system of claim 14, wherein the first cost function isdifferent from the second cost function.
 16. The system of claim 15,wherein the waveform shape is different from the second waveform shape.17. The system of claim 16, wherein the second predetermined costfunction improves at least one of efficiency and efficacy.
 18. A methodof treating a neurological condition, the method comprising the stepsof: selecting a waveform shape based upon a system constraint of a pulsegenerator; and applying a temporal pattern of stimulation to a targetedneurological tissue region using the pulse generator, the temporalpattern of stimulation comprising a plurality of pulses havingnon-regular, non-random, differing inter-pulse intervals therebetween.19. The method of claim 18, wherein each temporal pattern of stimulationcomprises an average frequency of less than 100 Hz.
 20. The method ofclaim 18, the waveform shape comprises at least one of rectangular,rising ramp, sinusoid, decreasing exponential, rising exponential, andcapacitive.
 21. The method of claim 18, wherein the system constraintincludes a constraint of a power source of the pulse generator.
 22. Themethod of claim 18, wherein the system constraint includes a memoryconstraint of a memory of the pulse generator.
 23. The method of claim18, wherein the system constraint includes energy consumption of thepulse generator.
 24. The method of claim 18, wherein the waveform shaperesults in an efficiency of about 99.5% for the pulse generator.
 25. Themethod of claim 18, wherein the waveform shape results in an efficiencyof about between 95% and 99.5% for the pulse generator.
 26. The methodof claim 18, further comprising the step of: repeating the applying stepin succession to treat a neurological symptom.
 27. The method of claim18, further comprising the steps of: assessing the temporal pattern ofstimulation applied; and applying a second temporal pattern ofstimulation to the targeted neurological tissue region using the pulsegenerator, the second temporal pattern of stimulation comprising asecond plurality of pulses having non-regular, non-random, differinginter-pulse intervals therebetween.
 28. The method of claim 27, whereinthe second temporal pattern of stimulation improves at least one ofefficiency and efficacy of the pulse generator.
 29. The method of claim27, wherein the second temporal pattern of stimulation improves at leastone of efficiency and efficacy of treating the neurological condition.30. The method of claim 27, wherein the temporal pattern of stimulationincludes is a first cost function and the second temporal pattern ofstimulation includes a second cost function, whereby the first costfunction is different from the second cost function.
 31. The method ofclaim 30, wherein the efficiency of the second cost function is greaterthan efficiency of the first cost function.
 32. The method of claim 30,wherein the efficacy of the second cost function is greater thanefficacy of the first cost function.